
from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


lowercase    :   Dict						=			logging.get_logger(__name__)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int		,    _lowerCamelCase					:     List[str])   ->       Optional[int]:






							'''simple docstring'''





							try:
														with open(_lowerCamelCase		,    "rb") as flax_state_f:
																					__UpperCamelCase      :  str       =   from_bytes(_lowerCamelCase		,    flax_state_f.read())
							except UnpicklingError as e:
														try:
																					with open(_lowerCamelCase) as f:
																												if f.read().startswith("version"):
																																			raise OSError(
																																			    "You seem to have cloned a repository without having git-lfs installed. Please"
																																			    " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
																																			    " folder you cloned.")
																												else:
																																			raise ValueError from e
														except (UnicodeDecodeError, ValueError):
																					raise EnvironmentError(F'Unable to convert {model_file} to Flax deserializable object. ')

							return load_flax_weights_in_pytorch_model(_lowerCamelCase		,    _lowerCamelCase)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[str]		,    _lowerCamelCase					:     Any)   ->       Optional[int]:






							'''simple docstring'''





							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
														    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
														    " instructions.")
														raise

							# check if we have bf16 weights
							__UpperCamelCase      :  Dict       =   flatten_dict(jax.tree_util.tree_map(lambda _lowerCamelCase: x.dtype == jnp.bfloataa		,    _lowerCamelCase)).values()
							if any(_lowerCamelCase):
														# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

														# and bf16 is not fully supported in PT yet.
														logger.warning(
														    "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
														    "before loading those in PyTorch model.")
														__UpperCamelCase      :  Optional[Any]       =   jax.tree_util.tree_map(
														    lambda _lowerCamelCase: params.astype(np.floataa) if params.dtype == jnp.bfloataa else params		,    _lowerCamelCase)

							__UpperCamelCase      :  Dict       =   ""

							__UpperCamelCase      :  int       =   flatten_dict(_lowerCamelCase		,    sep=".")
							__UpperCamelCase      :  Optional[Any]       =   pt_model.state_dict()

							# keep track of unexpected & missing keys
							__UpperCamelCase      :  Tuple       =   []
							__UpperCamelCase      :  Optional[Any]       =   set(pt_model_dict.keys())

							for flax_key_tuple, flax_tensor in flax_state_dict.items():
														__UpperCamelCase      :  str       =   flax_key_tuple.split(".")

														if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
																					__UpperCamelCase      :  str       =   flax_key_tuple_array[:-1] + ["weight"]
																					__UpperCamelCase      :  Tuple       =   jnp.transpose(_lowerCamelCase		,    (3, 2, 0, 1))
														elif flax_key_tuple_array[-1] == "kernel":
																					__UpperCamelCase      :  Optional[int]       =   flax_key_tuple_array[:-1] + ["weight"]
																					__UpperCamelCase      :  List[Any]       =   flax_tensor.T
														elif flax_key_tuple_array[-1] == "scale":
																					__UpperCamelCase      :  Dict       =   flax_key_tuple_array[:-1] + ["weight"]

														if "time_embedding" not in flax_key_tuple_array:
																					for i, flax_key_tuple_string in enumerate(_lowerCamelCase):
																												__UpperCamelCase      :  Optional[Any]       =   (
																												    flax_key_tuple_string.replace("_0"		,    ".0")
																												    .replace("_1"		,    ".1")
																												    .replace("_2"		,    ".2")
																												    .replace("_3"		,    ".3")
																												    .replace("_4"		,    ".4")
																												    .replace("_5"		,    ".5")
																												    .replace("_6"		,    ".6")
																												    .replace("_7"		,    ".7")
																												    .replace("_8"		,    ".8")
																												    .replace("_9"		,    ".9")
																												)

														__UpperCamelCase      :  List[Any]       =   ".".join(_lowerCamelCase)

														if flax_key in pt_model_dict:
																					if flax_tensor.shape != pt_model_dict[flax_key].shape:
																												raise ValueError(
																												    F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
																												    F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.')
																					else:
																												# add weight to pytorch dict
																												__UpperCamelCase      :  str       =   np.asarray(_lowerCamelCase) if not isinstance(_lowerCamelCase		,    np.ndarray) else flax_tensor
																												__UpperCamelCase      :  List[Any]       =   torch.from_numpy(_lowerCamelCase)
																												# remove from missing keys
																												missing_keys.remove(_lowerCamelCase)
														else:
																					# weight is not expected by PyTorch model
																					unexpected_keys.append(_lowerCamelCase)

							pt_model.load_state_dict(_lowerCamelCase)

							# re-transform missing_keys to list
							__UpperCamelCase      :  str       =   list(_lowerCamelCase)

							if len(_lowerCamelCase) > 0:
														logger.warning(
														    "Some weights of the Flax model were not used when initializing the PyTorch model"
														    F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
														    F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
														    " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
														    F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
														    " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
														    " FlaxBertForSequenceClassification model).")
							if len(_lowerCamelCase) > 0:
														logger.warning(
														    F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
														    F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
														    " use it for predictions and inference.")

							return pt_model

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase    :   Tuple						=			logging.getLogger(__name__)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Dict	,  a						:Union[str, Any]=-1		)						->					List[str]:
								# in NER datasets, the last column is usually reserved for NER label
								__UpperCamelCase      :  str       =   label_idx







	def   _lowerCamelCase							(   self						:str	,  a						:Tuple	,  a						:Union[Split, str]		)						->					List[InputExample]:
								if isinstance(a	,  a		):
															__UpperCamelCase      :  Dict       =   mode.value
								__UpperCamelCase      :  Union[str, Any]       =   os.path.join(a	,  f'{mode}.txt'		)
								__UpperCamelCase      :  Any       =   1
								__UpperCamelCase      :  List[str]       =   []
								with open(a	,  encoding="utf-8"		) as f:
															__UpperCamelCase      :  Tuple       =   []
															__UpperCamelCase      :  Dict       =   []
															for line in f:
																						if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
																													if words:
																																				examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
																																				guid_index += 1
																																				__UpperCamelCase      :  Dict       =   []
																																				__UpperCamelCase      :  List[str]       =   []
																						else:
																													__UpperCamelCase      :  Union[str, Any]       =   line.split(" "		)
																													words.append(splits[0]		)
																													if len(a		) > 1:
																																				labels.append(splits[self.label_idx].replace("\n"	,  ""		)		)
																													else:
																																				# Examples could have no label for mode = "test"
																																				labels.append("O"		)
															if words:
																						examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
								return examples







	def   _lowerCamelCase							(   self						:int	,  a						:TextIO	,  a						:TextIO	,  a						:List		)						->					Optional[Any]:
								__UpperCamelCase      :  Tuple       =   0
								for line in test_input_reader:
															if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
																						writer.write(a		)
																						if not preds_list[example_id]:
																													example_id += 1
															elif preds_list[example_id]:
																						__UpperCamelCase      :  Union[str, Any]       =   line.split()[0] + " " + preds_list[example_id].pop(0		) + "\n"
																						writer.write(a		)
															else:
																						logger.warning("Maximum sequence length exceeded: No prediction for '%s'."	,  line.split()[0]		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						__UpperCamelCase      :  List[str]       =   f.read().splitlines()
															if "O" not in labels:
																						__UpperCamelCase      :  Any       =   ["O"] + labels
															return labels
								else:
															return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:List[str]		)						->					int:
								# in CONLL2003 dataset chunk column is second-to-last
								super().__init__(label_idx=-2		)







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						__UpperCamelCase      :  Optional[Any]       =   f.read().splitlines()
															if "O" not in labels:
																						__UpperCamelCase      :  int       =   ["O"] + labels
															return labels
								else:
															return [
															    "O",
															    "B-ADVP",
															    "B-INTJ",
															    "B-LST",
															    "B-PRT",
															    "B-NP",
															    "B-SBAR",
															    "B-VP",
															    "B-ADJP",
															    "B-CONJP",
															    "B-PP",
															    "I-ADVP",
															    "I-INTJ",
															    "I-LST",
															    "I-PRT",
															    "I-NP",
															    "I-SBAR",
															    "I-VP",
															    "I-ADJP",
															    "I-CONJP",
															    "I-PP",
															]




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:List[str]	,  a						:Union[str, Any]	,  a						:Union[Split, str]		)						->					List[InputExample]:
								if isinstance(a	,  a		):
															__UpperCamelCase      :  Optional[Any]       =   mode.value
								__UpperCamelCase      :  List[str]       =   os.path.join(a	,  f'{mode}.txt'		)
								__UpperCamelCase      :  Dict       =   1
								__UpperCamelCase      :  List[str]       =   []

								with open(a	,  encoding="utf-8"		) as f:
															for sentence in parse_incr(a		):
																						__UpperCamelCase      :  Optional[int]       =   []
																						__UpperCamelCase      :  int       =   []
																						for token in sentence:
																													words.append(token["form"]		)
																													labels.append(token["upos"]		)
																						assert len(a		) == len(a		)
																						if words:
																													examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
																													guid_index += 1
								return examples







	def   _lowerCamelCase							(   self						:List[Any]	,  a						:TextIO	,  a						:TextIO	,  a						:List		)						->					str:
								__UpperCamelCase      :  List[Any]       =   0
								for sentence in parse_incr(a		):
															__UpperCamelCase      :  Tuple       =   preds_list[example_id]
															__UpperCamelCase      :  Dict       =   ""
															for token in sentence:
																						out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0		)}) '
															out += "\n"
															writer.write(a		)
															example_id += 1







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						return f.read().splitlines()
								else:
															return [
															    "ADJ",
															    "ADP",
															    "ADV",
															    "AUX",
															    "CCONJ",
															    "DET",
															    "INTJ",
															    "NOUN",
															    "NUM",
															    "PART",
															    "PRON",
															    "PROPN",
															    "PUNCT",
															    "SCONJ",
															    "SYM",
															    "VERB",
															    "X",
															]